Overview

Dataset statistics

Number of variables33
Number of observations343
Missing cells719
Missing cells (%)6.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory88.6 KiB
Average record size in memory264.4 B

Variable types

Categorical27
Numeric6

Warnings

Address has a high cardinality: 342 distinct values High cardinality
City has a high cardinality: 142 distinct values High cardinality
Neighborhood has a high cardinality: 214 distinct values High cardinality
Location Sales Volume Actual has a high cardinality: 125 distinct values High cardinality
Location Sales Volume Range is highly correlated with Contract Labor Expenses and 4 other fieldsHigh correlation
Metro Area is highly correlated with County and 1 other fieldsHigh correlation
Contract Labor Expenses is highly correlated with Location Sales Volume Range and 3 other fieldsHigh correlation
Insurance Expenses is highly correlated with Computer Expenses and 1 other fieldsHigh correlation
Computer Expenses is highly correlated with Insurance Expenses and 1 other fieldsHigh correlation
Advertising Expenses is highly correlated with Purchase Print Expenses and 1 other fieldsHigh correlation
Payroll and Benefits Expenses is highly correlated with Accounting Expenses and 2 other fieldsHigh correlation
Location Employee Size Range is highly correlated with Contract Labor ExpensesHigh correlation
Corporate Sales Volume Actual is highly correlated with Location Sales Volume Range and 2 other fieldsHigh correlation
County is highly correlated with Metro Area and 1 other fieldsHigh correlation
Accounting Expenses is highly correlated with Location Sales Volume Range and 3 other fieldsHigh correlation
Purchase Print Expenses is highly correlated with Location Sales Volume Range and 4 other fieldsHigh correlation
Company Name is highly correlated with Own or LeaseHigh correlation
Legal Expenses is highly correlated with Insurance Expenses and 2 other fieldsHigh correlation
Office Supplies Expense is highly correlated with Location Sales Volume Range and 4 other fieldsHigh correlation
Telcom Expenses is highly correlated with Legal ExpensesHigh correlation
State is highly correlated with Metro Area and 1 other fieldsHigh correlation
Own or Lease is highly correlated with Contract Labor Expenses and 2 other fieldsHigh correlation
Advertising Expenses has 5 (1.5%) missing values Missing
Accounting Expenses has 5 (1.5%) missing values Missing
Neighborhood has 88 (25.7%) missing values Missing
ZIP Four has 13 (3.8%) missing values Missing
Year Established has 328 (95.6%) missing values Missing
Computer Expenses has 5 (1.5%) missing values Missing
Contract Labor Expenses has 5 (1.5%) missing values Missing
Insurance Expenses has 5 (1.5%) missing values Missing
Legal Expenses has 5 (1.5%) missing values Missing
Location Sales Volume Range has 6 (1.7%) missing values Missing
Management/Administration Expenses has 5 (1.5%) missing values Missing
Office Supplies Expense has 5 (1.5%) missing values Missing
Own or Lease has 215 (62.7%) missing values Missing
Package Container Expense has 5 (1.5%) missing values Missing
Payroll and Benefits Expenses has 4 (1.2%) missing values Missing
Purchase Print Expenses has 5 (1.5%) missing values Missing
Rent Expenses has 5 (1.5%) missing values Missing
Telcom Expenses has 5 (1.5%) missing values Missing
Utilities Expenses has 5 (1.5%) missing values Missing
Address is uniformly distributed Uniform
Neighborhood is uniformly distributed Uniform

Reproduction

Analysis started2021-01-21 00:51:57.525101
Analysis finished2021-01-21 00:52:18.033345
Duration20.51 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

Advertising Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$20,000 to $50,000
285 
$10,000 to $20,000
36 
$50,000 to $100,000
 
14
Over $250,000
 
2
$5,000 to $10,000
 
1

Length

Max length19
Median length18
Mean length18.00887574
Min length13

Characters and Unicode

Total characters6087
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$20,000 to $50,000
2nd row$20,000 to $50,000
3rd row$20,000 to $50,000
4th row$20,000 to $50,000
5th row$20,000 to $50,000
ValueCountFrequency (%)
$20,000 to $50,000285
83.1%
$10,000 to $20,00036
 
10.5%
$50,000 to $100,00014
 
4.1%
Over $250,0002
 
0.6%
$5,000 to $10,0001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:18.438213image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:18.580863image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to336
33.2%
20,000321
31.7%
50,000299
29.5%
10,00037
 
3.7%
100,00014
 
1.4%
250,0002
 
0.2%
over2
 
0.2%
5,0001
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02709
44.5%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
t336
 
5.5%
o336
 
5.5%
2323
 
5.3%
5302
 
5.0%
151
 
0.8%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3385
55.6%
Lowercase Letter678
 
11.1%
Currency Symbol674
 
11.1%
Other Punctuation674
 
11.1%
Space Separator674
 
11.1%
Uppercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.6%
o336
49.6%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02709
80.0%
2323
 
9.5%
5302
 
8.9%
151
 
1.5%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,674
100.0%
ValueCountFrequency (%)
674
100.0%
ValueCountFrequency (%)
O2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5407
88.8%
Latin680
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
02709
50.1%
$674
 
12.5%
,674
 
12.5%
674
 
12.5%
2323
 
6.0%
5302
 
5.6%
151
 
0.9%
ValueCountFrequency (%)
t336
49.4%
o336
49.4%
O2
 
0.3%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6087
100.0%

Most frequent character per block

ValueCountFrequency (%)
02709
44.5%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
t336
 
5.5%
o336
 
5.5%
2323
 
5.3%
5302
 
5.0%
151
 
0.8%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Accounting Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$2,500 to $5,000
258 
$1,000 to $2,500
52 
$5,000 to $10,000
 
25
Over $25,000
 
2
$500 to $1,000
 
1

Length

Max length17
Median length16
Mean length16.0443787
Min length12

Characters and Unicode

Total characters5423
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$2,500 to $5,000
2nd row$2,500 to $5,000
3rd row$2,500 to $5,000
4th row$2,500 to $5,000
5th row$2,500 to $5,000
ValueCountFrequency (%)
$2,500 to $5,000258
75.2%
$1,000 to $2,50052
 
15.2%
$5,000 to $10,00025
 
7.3%
Over $25,0002
 
0.6%
$500 to $1,0001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:19.066941image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:19.207848image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to336
33.2%
2,500310
30.6%
5,000283
28.0%
1,00053
 
5.2%
10,00025
 
2.5%
25,0002
 
0.2%
over2
 
0.2%
5001
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01736
32.0%
$674
 
12.4%
674
 
12.4%
,673
 
12.4%
5596
 
11.0%
t336
 
6.2%
o336
 
6.2%
2312
 
5.8%
178
 
1.4%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2722
50.2%
Lowercase Letter678
 
12.5%
Currency Symbol674
 
12.4%
Space Separator674
 
12.4%
Other Punctuation673
 
12.4%
Uppercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.6%
o336
49.6%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
01736
63.8%
5596
 
21.9%
2312
 
11.5%
178
 
2.9%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,673
100.0%
ValueCountFrequency (%)
674
100.0%
ValueCountFrequency (%)
O2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4743
87.5%
Latin680
 
12.5%

Most frequent character per script

ValueCountFrequency (%)
01736
36.6%
$674
 
14.2%
674
 
14.2%
,673
 
14.2%
5596
 
12.6%
2312
 
6.6%
178
 
1.6%
ValueCountFrequency (%)
t336
49.4%
o336
49.4%
O2
 
0.3%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5423
100.0%

Most frequent character per block

ValueCountFrequency (%)
01736
32.0%
$674
 
12.4%
674
 
12.4%
,673
 
12.4%
5596
 
11.0%
t336
 
6.2%
o336
 
6.2%
2312
 
5.8%
178
 
1.4%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Address
Categorical

HIGH CARDINALITY
UNIFORM

Distinct342
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
5707 Blue Lagoon Dr
 
2
13201 S Dixie Hwy
 
1
826 Central Park Ave
 
1
21603 Merrick Blvd
 
1
2343 Utica Ave # 1
 
1
Other values (337)
337 

Length

Max length30
Median length17
Mean length17.23906706
Min length8

Characters and Unicode

Total characters5913
Distinct characters60
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique341 ?
Unique (%)99.4%

Sample

1st row1104 Sunrise Hwy
2nd row3310 21st St
3rd row3310 Astoria Blvd
4th row335 E Main St
5th row22210 Northern Blvd
ValueCountFrequency (%)
5707 Blue Lagoon Dr2
 
0.6%
13201 S Dixie Hwy1
 
0.3%
826 Central Park Ave1
 
0.3%
21603 Merrick Blvd1
 
0.3%
2343 Utica Ave # 11
 
0.3%
5501 Corporate Way1
 
0.3%
1300 Deer Park Ave1
 
0.3%
1607 Westchester Ave # Radiosh1
 
0.3%
1521 W Boynton Beach Blvd1
 
0.3%
18750 NW 2nd Ave1
 
0.3%
Other values (332)332
96.8%
2021-01-21T00:52:19.787308image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ave86
 
6.7%
st64
 
5.0%
blvd52
 
4.0%
rd44
 
3.4%
w38
 
2.9%
28
 
2.2%
s28
 
2.2%
hwy26
 
2.0%
sw21
 
1.6%
nw20
 
1.6%
Other values (579)883
68.4%

Most occurring characters

ValueCountFrequency (%)
947
 
16.0%
e318
 
5.4%
1301
 
5.1%
t261
 
4.4%
0251
 
4.2%
a202
 
3.4%
d189
 
3.2%
l186
 
3.1%
r181
 
3.1%
2179
 
3.0%
Other values (50)2898
49.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2584
43.7%
Decimal Number1485
25.1%
Space Separator947
 
16.0%
Uppercase Letter864
 
14.6%
Other Punctuation30
 
0.5%
Dash Punctuation3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e318
12.3%
t261
10.1%
a202
 
7.8%
d189
 
7.3%
l186
 
7.2%
r181
 
7.0%
n162
 
6.3%
o162
 
6.3%
i153
 
5.9%
v150
 
5.8%
Other values (14)620
24.0%
ValueCountFrequency (%)
S142
16.4%
A100
11.6%
W92
10.6%
B90
10.4%
R69
8.0%
N48
 
5.6%
H46
 
5.3%
M36
 
4.2%
C36
 
4.2%
P34
 
3.9%
Other values (13)171
19.8%
ValueCountFrequency (%)
1301
20.3%
0251
16.9%
2179
12.1%
5152
10.2%
3120
 
8.1%
7113
 
7.6%
4102
 
6.9%
6102
 
6.9%
993
 
6.3%
872
 
4.8%
ValueCountFrequency (%)
947
100.0%
ValueCountFrequency (%)
#30
100.0%
ValueCountFrequency (%)
-3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3448
58.3%
Common2465
41.7%

Most frequent character per script

ValueCountFrequency (%)
e318
 
9.2%
t261
 
7.6%
a202
 
5.9%
d189
 
5.5%
l186
 
5.4%
r181
 
5.2%
n162
 
4.7%
o162
 
4.7%
i153
 
4.4%
v150
 
4.4%
Other values (37)1484
43.0%
ValueCountFrequency (%)
947
38.4%
1301
 
12.2%
0251
 
10.2%
2179
 
7.3%
5152
 
6.2%
3120
 
4.9%
7113
 
4.6%
4102
 
4.1%
6102
 
4.1%
993
 
3.8%
Other values (3)105
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5913
100.0%

Most frequent character per block

ValueCountFrequency (%)
947
 
16.0%
e318
 
5.4%
1301
 
5.1%
t261
 
4.4%
0251
 
4.2%
a202
 
3.4%
d189
 
3.2%
l186
 
3.1%
r181
 
3.1%
2179
 
3.0%
Other values (50)2898
49.0%

City
Categorical

HIGH CARDINALITY

Distinct142
Distinct (%)41.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Miami
44 
Brooklyn
33 
Bronx
 
18
New York
 
13
West Palm Beach
 
11
Other values (137)
224 

Length

Max length19
Median length8
Mean length9.244897959
Min length5

Characters and Unicode

Total characters3171
Distinct characters49
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)27.7%

Sample

1st rowAmityville
2nd rowAstoria
3rd rowAstoria
4th rowBay Shore
5th rowBayside
ValueCountFrequency (%)
Miami44
 
12.8%
Brooklyn33
 
9.6%
Bronx18
 
5.2%
New York13
 
3.8%
West Palm Beach11
 
3.2%
Staten Island10
 
2.9%
Fort Lauderdale9
 
2.6%
Hialeah8
 
2.3%
Jamaica7
 
2.0%
Davie6
 
1.7%
Other values (132)184
53.6%
2021-01-21T00:52:20.255261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
miami57
 
11.4%
brooklyn33
 
6.6%
beach31
 
6.2%
bronx18
 
3.6%
new16
 
3.2%
west15
 
3.0%
palm14
 
2.8%
york13
 
2.6%
lauderdale12
 
2.4%
island10
 
2.0%
Other values (150)281
56.2%

Most occurring characters

ValueCountFrequency (%)
a337
 
10.6%
e256
 
8.1%
o242
 
7.6%
i218
 
6.9%
r196
 
6.2%
n194
 
6.1%
l172
 
5.4%
157
 
5.0%
t149
 
4.7%
m110
 
3.5%
Other values (39)1140
36.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2514
79.3%
Uppercase Letter500
 
15.8%
Space Separator157
 
5.0%

Most frequent character per category

ValueCountFrequency (%)
a337
13.4%
e256
10.2%
o242
9.6%
i218
 
8.7%
r196
 
7.8%
n194
 
7.7%
l172
 
6.8%
t149
 
5.9%
m110
 
4.4%
s94
 
3.7%
Other values (15)546
21.7%
ValueCountFrequency (%)
B103
20.6%
M67
13.4%
P42
8.4%
S34
 
6.8%
H32
 
6.4%
L29
 
5.8%
N27
 
5.4%
C26
 
5.2%
W26
 
5.2%
F19
 
3.8%
Other values (13)95
19.0%
ValueCountFrequency (%)
157
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3014
95.0%
Common157
 
5.0%

Most frequent character per script

ValueCountFrequency (%)
a337
 
11.2%
e256
 
8.5%
o242
 
8.0%
i218
 
7.2%
r196
 
6.5%
n194
 
6.4%
l172
 
5.7%
t149
 
4.9%
m110
 
3.6%
B103
 
3.4%
Other values (38)1037
34.4%
ValueCountFrequency (%)
157
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3171
100.0%

Most frequent character per block

ValueCountFrequency (%)
a337
 
10.6%
e256
 
8.1%
o242
 
7.6%
i218
 
6.9%
r196
 
6.2%
n194
 
6.1%
l172
 
5.4%
157
 
5.0%
t149
 
4.7%
m110
 
3.5%
Other values (39)1140
36.0%

State
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
NY
179 
FL
164 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters686
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNY
2nd rowNY
3rd rowNY
4th rowNY
5th rowNY
ValueCountFrequency (%)
NY179
52.2%
FL164
47.8%
2021-01-21T00:52:20.669890image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:20.792616image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
ny179
52.2%
fl164
47.8%

Most occurring characters

ValueCountFrequency (%)
N179
26.1%
Y179
26.1%
F164
23.9%
L164
23.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter686
100.0%

Most frequent character per category

ValueCountFrequency (%)
N179
26.1%
Y179
26.1%
F164
23.9%
L164
23.9%

Most occurring scripts

ValueCountFrequency (%)
Latin686
100.0%

Most frequent character per script

ValueCountFrequency (%)
N179
26.1%
Y179
26.1%
F164
23.9%
L164
23.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII686
100.0%

Most frequent character per block

ValueCountFrequency (%)
N179
26.1%
Y179
26.1%
F164
23.9%
L164
23.9%

County
Categorical

HIGH CORRELATION

Distinct13
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Miami Dade
82 
Broward
49 
Kings
34 
Palm Beach
33 
Suffolk
30 
Other values (8)
115 

Length

Max length11
Median length7
Mean length7.752186589
Min length5

Characters and Unicode

Total characters2659
Distinct characters31
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSuffolk
2nd rowQueens
3rd rowQueens
4th rowSuffolk
5th rowQueens
ValueCountFrequency (%)
Miami Dade82
23.9%
Broward49
14.3%
Kings34
9.9%
Palm Beach33
9.6%
Suffolk30
 
8.7%
Queens29
 
8.5%
Nassau27
 
7.9%
Bronx18
 
5.2%
New York13
 
3.8%
Westchester12
 
3.5%
Other values (3)16
 
4.7%
2021-01-21T00:52:21.173975image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dade82
17.4%
miami82
17.4%
broward49
10.4%
kings34
7.2%
palm33
7.0%
beach33
7.0%
suffolk30
 
6.4%
queens29
 
6.2%
nassau27
 
5.7%
bronx18
 
3.8%
Other values (6)54
11.5%

Most occurring characters

ValueCountFrequency (%)
a339
 
12.7%
e222
 
8.3%
i208
 
7.8%
d145
 
5.5%
s141
 
5.3%
r141
 
5.3%
128
 
4.8%
m127
 
4.8%
o124
 
4.7%
B100
 
3.8%
Other values (21)984
37.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2060
77.5%
Uppercase Letter471
 
17.7%
Space Separator128
 
4.8%

Most frequent character per category

ValueCountFrequency (%)
a339
16.5%
e222
10.8%
i208
10.1%
d145
 
7.0%
s141
 
6.8%
r141
 
6.8%
m127
 
6.2%
o124
 
6.0%
n97
 
4.7%
u88
 
4.3%
Other values (9)428
20.8%
ValueCountFrequency (%)
B100
21.2%
M82
17.4%
D82
17.4%
N40
 
8.5%
P35
 
7.4%
K34
 
7.2%
S30
 
6.4%
Q29
 
6.2%
R14
 
3.0%
Y13
 
2.8%
ValueCountFrequency (%)
128
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin2531
95.2%
Common128
 
4.8%

Most frequent character per script

ValueCountFrequency (%)
a339
 
13.4%
e222
 
8.8%
i208
 
8.2%
d145
 
5.7%
s141
 
5.6%
r141
 
5.6%
m127
 
5.0%
o124
 
4.9%
B100
 
4.0%
n97
 
3.8%
Other values (20)887
35.0%
ValueCountFrequency (%)
128
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2659
100.0%

Most frequent character per block

ValueCountFrequency (%)
a339
 
12.7%
e222
 
8.3%
i208
 
7.8%
d145
 
5.5%
s141
 
5.3%
r141
 
5.3%
128
 
4.8%
m127
 
4.8%
o124
 
4.7%
B100
 
3.8%
Other values (21)984
37.0%

Metro Area
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Nw Yrk, NY-NJ-PA
179 
Miami-Ft Ldr, FL
164 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters5488
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNw Yrk, NY-NJ-PA
2nd rowNw Yrk, NY-NJ-PA
3rd rowNw Yrk, NY-NJ-PA
4th rowNw Yrk, NY-NJ-PA
5th rowNw Yrk, NY-NJ-PA
ValueCountFrequency (%)
Nw Yrk, NY-NJ-PA179
52.2%
Miami-Ft Ldr, FL164
47.8%
2021-01-21T00:52:21.576580image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:21.703358image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
yrk179
17.4%
ny-nj-pa179
17.4%
nw179
17.4%
ldr164
15.9%
fl164
15.9%
miami-ft164
15.9%

Most occurring characters

ValueCountFrequency (%)
686
12.5%
N537
 
9.8%
-522
 
9.5%
Y358
 
6.5%
r343
 
6.2%
,343
 
6.2%
i328
 
6.0%
F328
 
6.0%
L328
 
6.0%
w179
 
3.3%
Other values (9)1536
28.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter2252
41.0%
Lowercase Letter1685
30.7%
Space Separator686
 
12.5%
Dash Punctuation522
 
9.5%
Other Punctuation343
 
6.2%

Most frequent character per category

ValueCountFrequency (%)
N537
23.8%
Y358
15.9%
F328
14.6%
L328
14.6%
J179
 
7.9%
P179
 
7.9%
A179
 
7.9%
M164
 
7.3%
ValueCountFrequency (%)
r343
20.4%
i328
19.5%
w179
10.6%
k179
10.6%
a164
9.7%
m164
9.7%
t164
9.7%
d164
9.7%
ValueCountFrequency (%)
686
100.0%
ValueCountFrequency (%)
,343
100.0%
ValueCountFrequency (%)
-522
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3937
71.7%
Common1551
 
28.3%

Most frequent character per script

ValueCountFrequency (%)
N537
13.6%
Y358
 
9.1%
r343
 
8.7%
i328
 
8.3%
F328
 
8.3%
L328
 
8.3%
w179
 
4.5%
k179
 
4.5%
J179
 
4.5%
P179
 
4.5%
Other values (6)999
25.4%
ValueCountFrequency (%)
686
44.2%
-522
33.7%
,343
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5488
100.0%

Most frequent character per block

ValueCountFrequency (%)
686
12.5%
N537
 
9.8%
-522
 
9.5%
Y358
 
6.5%
r343
 
6.2%
,343
 
6.2%
i328
 
6.0%
F328
 
6.0%
L328
 
6.0%
w179
 
3.3%
Other values (9)1536
28.0%

Neighborhood
Categorical

HIGH CARDINALITY
MISSING
UNIFORM

Distinct214
Distinct (%)83.9%
Missing88
Missing (%)25.7%
Memory size2.8 KiB
Allapattah
 
4
Jackson Heights
 
4
East New York
 
3
Canarsie
 
3
University Park
 
3
Other values (209)
238 

Length

Max length31
Median length12
Mean length12.98039216
Min length5

Characters and Unicode

Total characters3310
Distinct characters56
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique182 ?
Unique (%)71.4%

Sample

1st rowNorth Amityville
2nd rowAstoria
3rd rowAstoria
4th rowBay Shore
5th rowBayside
ValueCountFrequency (%)
Allapattah4
 
1.2%
Jackson Heights4
 
1.2%
East New York3
 
0.9%
Canarsie3
 
0.9%
University Park3
 
0.9%
Financial District3
 
0.9%
East Harlem3
 
0.9%
Woodmere2
 
0.6%
Farmingville2
 
0.6%
Flatbush2
 
0.6%
Other values (204)226
65.9%
(Missing)88
 
25.7%
2021-01-21T00:52:22.150134image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park23
 
4.7%
east17
 
3.5%
heights14
 
2.9%
west13
 
2.7%
north13
 
2.7%
village9
 
1.9%
district8
 
1.6%
downtown7
 
1.4%
bay7
 
1.4%
lakes7
 
1.4%
Other values (243)368
75.7%

Most occurring characters

ValueCountFrequency (%)
e302
 
9.1%
a286
 
8.6%
231
 
7.0%
t215
 
6.5%
r211
 
6.4%
o207
 
6.3%
l192
 
5.8%
i190
 
5.7%
s177
 
5.3%
n170
 
5.1%
Other values (46)1129
34.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2580
77.9%
Uppercase Letter489
 
14.8%
Space Separator231
 
7.0%
Dash Punctuation4
 
0.1%
Other Punctuation3
 
0.1%
Decimal Number3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e302
11.7%
a286
11.1%
t215
 
8.3%
r211
 
8.2%
o207
 
8.0%
l192
 
7.4%
i190
 
7.4%
s177
 
6.9%
n170
 
6.6%
h79
 
3.1%
Other values (16)551
21.4%
ValueCountFrequency (%)
P53
 
10.8%
C52
 
10.6%
B43
 
8.8%
H40
 
8.2%
S34
 
7.0%
W29
 
5.9%
E27
 
5.5%
L26
 
5.3%
N24
 
4.9%
M21
 
4.3%
Other values (14)140
28.6%
ValueCountFrequency (%)
'2
66.7%
.1
33.3%
ValueCountFrequency (%)
42
66.7%
11
33.3%
ValueCountFrequency (%)
231
100.0%
ValueCountFrequency (%)
-4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3069
92.7%
Common241
 
7.3%

Most frequent character per script

ValueCountFrequency (%)
e302
 
9.8%
a286
 
9.3%
t215
 
7.0%
r211
 
6.9%
o207
 
6.7%
l192
 
6.3%
i190
 
6.2%
s177
 
5.8%
n170
 
5.5%
h79
 
2.6%
Other values (40)1040
33.9%
ValueCountFrequency (%)
231
95.9%
-4
 
1.7%
'2
 
0.8%
42
 
0.8%
.1
 
0.4%
11
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII3310
100.0%

Most frequent character per block

ValueCountFrequency (%)
e302
 
9.1%
a286
 
8.6%
231
 
7.0%
t215
 
6.5%
r211
 
6.4%
o207
 
6.3%
l192
 
5.8%
i190
 
5.7%
s177
 
5.3%
n170
 
5.1%
Other values (46)1129
34.1%

ZIP Code
Real number (ℝ≥0)

Distinct257
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21678.16035
Minimum10001
Maximum33484
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:22.371115image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum10001
5-th percentile10307.5
Q111223.5
median11787
Q333165
95-th percentile33427.8
Maximum33484
Range23483
Interquartile range (IQR)21941.5

Descriptive statistics

Standard deviation11066.18846
Coefficient of variation (CV)0.5104763634
Kurtosis-1.998906284
Mean21678.16035
Median Absolute Deviation (MAD)1758
Skewness0.084241258
Sum7435609
Variance122460527.1
MonotocityNot monotonic
2021-01-21T00:52:22.614735image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
331654
 
1.2%
331574
 
1.2%
112364
 
1.2%
331664
 
1.2%
331264
 
1.2%
103144
 
1.2%
333513
 
0.9%
110033
 
0.9%
330163
 
0.9%
330153
 
0.9%
Other values (247)307
89.5%
ValueCountFrequency (%)
100011
0.3%
100021
0.3%
100041
0.3%
100061
0.3%
100071
0.3%
ValueCountFrequency (%)
334841
0.3%
334831
0.3%
334701
0.3%
334632
0.6%
334622
0.6%

ZIP Four
Real number (ℝ≥0)

MISSING

Distinct312
Distinct (%)94.5%
Missing13
Missing (%)3.8%
Infinite0
Infinite (%)0.0%
Mean3679.99697
Minimum1
Maximum8410
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:22.864317image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1143.95
Q12305
median3412.5
Q34800.75
95-th percentile6953.6
Maximum8410
Range8409
Interquartile range (IQR)2495.75

Descriptive statistics

Standard deviation1774.287602
Coefficient of variation (CV)0.4821437672
Kurtosis-0.3377454182
Mean3679.99697
Median Absolute Deviation (MAD)1234
Skewness0.5304775266
Sum1214399
Variance3148096.495
MonotocityNot monotonic
2021-01-21T00:52:23.104365image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44122
 
0.6%
42392
 
0.6%
42002
 
0.6%
46092
 
0.6%
31092
 
0.6%
27022
 
0.6%
21052
 
0.6%
15292
 
0.6%
35042
 
0.6%
15252
 
0.6%
Other values (302)310
90.4%
(Missing)13
 
3.8%
ValueCountFrequency (%)
11
0.3%
10021
0.3%
10031
0.3%
10041
0.3%
10051
0.3%
ValueCountFrequency (%)
84101
0.3%
83181
0.3%
82701
0.3%
82151
0.3%
81431
0.3%

Year Established
Real number (ℝ≥0)

MISSING

Distinct9
Distinct (%)60.0%
Missing328
Missing (%)95.6%
Infinite0
Infinite (%)0.0%
Mean1994.933333
Minimum1954
Maximum2019
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:23.309903image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1954
5-th percentile1954
Q11976
median2002
Q32017
95-th percentile2018.3
Maximum2019
Range65
Interquartile range (IQR)41

Descriptive statistics

Standard deviation26.64439546
Coefficient of variation (CV)0.01335603301
Kurtosis-0.9759928889
Mean1994.933333
Median Absolute Deviation (MAD)15
Skewness-0.8882010631
Sum29924
Variance709.9238095
MonotocityNot monotonic
2021-01-21T00:52:23.474302image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
19544
 
1.2%
20182
 
0.6%
20172
 
0.6%
20002
 
0.6%
20021
 
0.3%
20191
 
0.3%
19981
 
0.3%
20031
 
0.3%
20161
 
0.3%
(Missing)328
95.6%
ValueCountFrequency (%)
19544
1.2%
19981
 
0.3%
20002
0.6%
20021
 
0.3%
20031
 
0.3%
ValueCountFrequency (%)
20191
0.3%
20182
0.6%
20172
0.6%
20161
0.3%
20031
0.3%

Years In Database
Real number (ℝ≥0)

Distinct36
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.08746356
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:23.673707image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median22
Q335.5
95-th percentile37
Maximum37
Range36
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation12.64233305
Coefficient of variation (CV)0.5995189047
Kurtosis-1.467933028
Mean21.08746356
Median Absolute Deviation (MAD)12
Skewness-0.04323448486
Sum7233
Variance159.8285851
MonotocityNot monotonic
2021-01-21T00:52:24.079290image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
3785
24.8%
1219
 
5.5%
1316
 
4.7%
215
 
4.4%
1015
 
4.4%
314
 
4.1%
2211
 
3.2%
811
 
3.2%
1510
 
2.9%
49
 
2.6%
Other values (26)138
40.2%
ValueCountFrequency (%)
18
2.3%
215
4.4%
314
4.1%
49
2.6%
54
 
1.2%
ValueCountFrequency (%)
3785
24.8%
361
 
0.3%
356
 
1.7%
344
 
1.2%
337
 
2.0%

Company Name
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
Burger King
339 
Burger King Holdings Inc
 
1
Burger King Management Office
 
1
Burger King Worldwide Inc
 
1
Veggie Burger King Corp
 
1

Length

Max length29
Median length11
Mean length11.16618076
Min length11

Characters and Unicode

Total characters3830
Distinct characters27
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)1.2%

Sample

1st rowBurger King
2nd rowBurger King
3rd rowBurger King
4th rowBurger King
5th rowBurger King
ValueCountFrequency (%)
Burger King339
98.8%
Burger King Holdings Inc1
 
0.3%
Burger King Management Office1
 
0.3%
Burger King Worldwide Inc1
 
0.3%
Veggie Burger King Corp1
 
0.3%
2021-01-21T00:52:24.536973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:24.685243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
burger343
49.4%
king343
49.4%
inc2
 
0.3%
worldwide1
 
0.1%
management1
 
0.1%
veggie1
 
0.1%
corp1
 
0.1%
holdings1
 
0.1%
office1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
g690
18.0%
r688
18.0%
351
9.2%
e349
9.1%
n348
9.1%
i347
9.1%
B343
9.0%
u343
9.0%
K343
9.0%
c3
 
0.1%
Other values (17)25
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2785
72.7%
Uppercase Letter694
 
18.1%
Space Separator351
 
9.2%

Most frequent character per category

ValueCountFrequency (%)
g690
24.8%
r688
24.7%
e349
12.5%
n348
12.5%
i347
12.5%
u343
12.3%
c3
 
0.1%
o3
 
0.1%
d3
 
0.1%
a2
 
0.1%
Other values (7)9
 
0.3%
ValueCountFrequency (%)
B343
49.4%
K343
49.4%
I2
 
0.3%
M1
 
0.1%
O1
 
0.1%
H1
 
0.1%
W1
 
0.1%
V1
 
0.1%
C1
 
0.1%
ValueCountFrequency (%)
351
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3479
90.8%
Common351
 
9.2%

Most frequent character per script

ValueCountFrequency (%)
g690
19.8%
r688
19.8%
e349
10.0%
n348
10.0%
i347
10.0%
B343
9.9%
u343
9.9%
K343
9.9%
c3
 
0.1%
o3
 
0.1%
Other values (16)22
 
0.6%
ValueCountFrequency (%)
351
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3830
100.0%

Most frequent character per block

ValueCountFrequency (%)
g690
18.0%
r688
18.0%
351
9.2%
e349
9.1%
n348
9.1%
i347
9.1%
B343
9.0%
u343
9.0%
K343
9.0%
c3
 
0.1%
Other values (17)25
 
0.7%

Computer Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$1,000 to $2,500
280 
$2,500 to $5,000
47 
$500 to $1,000
 
8
Over $50,000
 
2
Less than $500
 
1

Length

Max length16
Median length16
Mean length15.92307692
Min length12

Characters and Unicode

Total characters5382
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$1,000 to $2,500
2nd row$1,000 to $2,500
3rd row$1,000 to $2,500
4th row$1,000 to $2,500
5th row$2,500 to $5,000
ValueCountFrequency (%)
$1,000 to $2,500280
81.6%
$2,500 to $5,00047
 
13.7%
$500 to $1,0008
 
2.3%
Over $50,0002
 
0.6%
Less than $5001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:25.105133image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:25.259973image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to335
33.1%
2,500327
32.3%
1,000288
28.5%
5,00047
 
4.6%
5009
 
0.9%
50,0002
 
0.2%
over2
 
0.2%
less1
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01685
31.3%
674
 
12.5%
$673
 
12.5%
,664
 
12.3%
5385
 
7.2%
t336
 
6.2%
o335
 
6.2%
2327
 
6.1%
1288
 
5.4%
e3
 
0.1%
Other values (8)12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2685
49.9%
Lowercase Letter683
 
12.7%
Space Separator674
 
12.5%
Currency Symbol673
 
12.5%
Other Punctuation664
 
12.3%
Uppercase Letter3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.2%
o335
49.0%
e3
 
0.4%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
01685
62.8%
5385
 
14.3%
2327
 
12.2%
1288
 
10.7%
ValueCountFrequency (%)
O2
66.7%
L1
33.3%
ValueCountFrequency (%)
$673
100.0%
ValueCountFrequency (%)
,664
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4696
87.3%
Latin686
 
12.7%

Most frequent character per script

ValueCountFrequency (%)
t336
49.0%
o335
48.8%
e3
 
0.4%
O2
 
0.3%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
L1
 
0.1%
h1
 
0.1%
a1
 
0.1%
ValueCountFrequency (%)
01685
35.9%
674
 
14.4%
$673
 
14.3%
,664
 
14.1%
5385
 
8.2%
2327
 
7.0%
1288
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5382
100.0%

Most frequent character per block

ValueCountFrequency (%)
01685
31.3%
674
 
12.5%
$673
 
12.5%
,664
 
12.3%
5385
 
7.2%
t336
 
6.2%
o335
 
6.2%
2327
 
6.1%
1288
 
5.4%
e3
 
0.1%
Other values (8)12
 
0.2%

Contract Labor Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$1,000 to $10,000
336 
Over $500,000
 
1
$250,000 to $500,000
 
1

Length

Max length20
Median length17
Mean length16.99704142
Min length13

Characters and Unicode

Total characters5745
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row$1,000 to $10,000
2nd row$1,000 to $10,000
3rd row$1,000 to $10,000
4th row$1,000 to $10,000
5th row$1,000 to $10,000
ValueCountFrequency (%)
$1,000 to $10,000336
98.0%
Over $500,0001
 
0.3%
$250,000 to $500,0001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:25.759719image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:25.904864image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to337
33.3%
10,000336
33.2%
1,000336
33.2%
500,0002
 
0.2%
250,0001
 
0.1%
over1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02366
41.2%
$675
 
11.7%
,675
 
11.7%
675
 
11.7%
1672
 
11.7%
t337
 
5.9%
o337
 
5.9%
53
 
0.1%
21
 
< 0.1%
O1
 
< 0.1%
Other values (3)3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3042
53.0%
Lowercase Letter677
 
11.8%
Currency Symbol675
 
11.7%
Other Punctuation675
 
11.7%
Space Separator675
 
11.7%
Uppercase Letter1
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t337
49.8%
o337
49.8%
v1
 
0.1%
e1
 
0.1%
r1
 
0.1%
ValueCountFrequency (%)
02366
77.8%
1672
 
22.1%
53
 
0.1%
21
 
< 0.1%
ValueCountFrequency (%)
$675
100.0%
ValueCountFrequency (%)
,675
100.0%
ValueCountFrequency (%)
675
100.0%
ValueCountFrequency (%)
O1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5067
88.2%
Latin678
 
11.8%

Most frequent character per script

ValueCountFrequency (%)
02366
46.7%
$675
 
13.3%
,675
 
13.3%
675
 
13.3%
1672
 
13.3%
53
 
0.1%
21
 
< 0.1%
ValueCountFrequency (%)
t337
49.7%
o337
49.7%
O1
 
0.1%
v1
 
0.1%
e1
 
0.1%
r1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII5745
100.0%

Most frequent character per block

ValueCountFrequency (%)
02366
41.2%
$675
 
11.7%
,675
 
11.7%
675
 
11.7%
1672
 
11.7%
t337
 
5.9%
o337
 
5.9%
53
 
0.1%
21
 
< 0.1%
O1
 
< 0.1%
Other values (3)3
 
0.1%

Corporate Sales Volume Actual
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
$0
340 
$803,666,000
 
1
$119,647,000
 
1
$1,146,300,000
 
1

Length

Max length14
Median length2
Mean length2.093294461
Min length2

Characters and Unicode

Total characters718
Distinct characters10
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row$0
2nd row$0
3rd row$0
4th row$0
5th row$0
ValueCountFrequency (%)
$0340
99.1%
$803,666,0001
 
0.3%
$119,647,0001
 
0.3%
$1,146,300,0001
 
0.3%
2021-01-21T00:52:26.327291image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:26.466752image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
0340
99.1%
1,146,300,0001
 
0.3%
119,647,0001
 
0.3%
803,666,0001
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0352
49.0%
$343
47.8%
,7
 
1.0%
65
 
0.7%
14
 
0.6%
42
 
0.3%
32
 
0.3%
91
 
0.1%
71
 
0.1%
81
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number368
51.3%
Currency Symbol343
47.8%
Other Punctuation7
 
1.0%

Most frequent character per category

ValueCountFrequency (%)
0352
95.7%
65
 
1.4%
14
 
1.1%
42
 
0.5%
32
 
0.5%
91
 
0.3%
71
 
0.3%
81
 
0.3%
ValueCountFrequency (%)
$343
100.0%
ValueCountFrequency (%)
,7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common718
100.0%

Most frequent character per script

ValueCountFrequency (%)
0352
49.0%
$343
47.8%
,7
 
1.0%
65
 
0.7%
14
 
0.6%
42
 
0.3%
32
 
0.3%
91
 
0.1%
71
 
0.1%
81
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII718
100.0%

Most frequent character per block

ValueCountFrequency (%)
0352
49.0%
$343
47.8%
,7
 
1.0%
65
 
0.7%
14
 
0.6%
42
 
0.3%
32
 
0.3%
91
 
0.1%
71
 
0.1%
81
 
0.1%

Insurance Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$10,000 to $25,000
280 
$25,000 to $50,000
47 
$5,000 to $10,000
 
8
Over $100,000
 
2
$2,500 to $5,000
 
1

Length

Max length18
Median length18
Mean length17.9408284
Min length13

Characters and Unicode

Total characters6064
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$10,000 to $25,000280
81.6%
$25,000 to $50,00047
 
13.7%
$5,000 to $10,0008
 
2.3%
Over $100,0002
 
0.6%
$2,500 to $5,0001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:26.844631image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:26.983730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to336
33.2%
25,000327
32.3%
10,000288
28.5%
50,00047
 
4.6%
5,0009
 
0.9%
over2
 
0.2%
100,0002
 
0.2%
2,5001
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02360
38.9%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
5384
 
6.3%
t336
 
5.5%
o336
 
5.5%
2328
 
5.4%
1290
 
4.8%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3362
55.4%
Lowercase Letter678
 
11.2%
Currency Symbol674
 
11.1%
Other Punctuation674
 
11.1%
Space Separator674
 
11.1%
Uppercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.6%
o336
49.6%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02360
70.2%
5384
 
11.4%
2328
 
9.8%
1290
 
8.6%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,674
100.0%
ValueCountFrequency (%)
674
100.0%
ValueCountFrequency (%)
O2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5384
88.8%
Latin680
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
02360
43.8%
$674
 
12.5%
,674
 
12.5%
674
 
12.5%
5384
 
7.1%
2328
 
6.1%
1290
 
5.4%
ValueCountFrequency (%)
t336
49.4%
o336
49.4%
O2
 
0.3%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6064
100.0%

Most frequent character per block

ValueCountFrequency (%)
02360
38.9%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
5384
 
6.3%
t336
 
5.5%
o336
 
5.5%
2328
 
5.4%
1290
 
4.8%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Last Updated On
Real number (ℝ≥0)

Distinct10
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean202009.8426
Minimum201906
Maximum202012
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:27.255121image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum201906
5-th percentile202007.1
Q1202011
median202011
Q3202011
95-th percentile202011
Maximum202012
Range106
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9.835414371
Coefficient of variation (CV)4.868779781 × 105
Kurtosis106.5070844
Mean202009.8426
Median Absolute Deviation (MAD)0
Skewness-10.29363028
Sum69289376
Variance96.73537585
MonotocityNot monotonic
2021-01-21T00:52:27.437566image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
202011309
90.1%
20201214
 
4.1%
2020078
 
2.3%
2020023
 
0.9%
2020082
 
0.6%
2020012
 
0.6%
2019062
 
0.6%
2020061
 
0.3%
2020041
 
0.3%
2019071
 
0.3%
ValueCountFrequency (%)
2019062
0.6%
2019071
 
0.3%
2020012
0.6%
2020023
0.9%
2020041
 
0.3%
ValueCountFrequency (%)
20201214
 
4.1%
202011309
90.1%
2020082
 
0.6%
2020078
 
2.3%
2020061
 
0.3%

Legal Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$1,000 to $2,500
286 
$2,500 to $5,000
40 
$500 to $1,000
 
9
Over $25,000
 
2
Less than $500
 
1

Length

Max length16
Median length16
Mean length15.91715976
Min length12

Characters and Unicode

Total characters5380
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$1,000 to $2,500
2nd row$1,000 to $2,500
3rd row$1,000 to $2,500
4th row$1,000 to $2,500
5th row$1,000 to $2,500
ValueCountFrequency (%)
$1,000 to $2,500286
83.4%
$2,500 to $5,00040
 
11.7%
$500 to $1,0009
 
2.6%
Over $25,0002
 
0.6%
Less than $5001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:27.873587image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:28.026607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to335
33.1%
2,500326
32.2%
1,000295
29.2%
5,00040
 
4.0%
50010
 
1.0%
25,0002
 
0.2%
over2
 
0.2%
less1
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01683
31.3%
674
12.5%
$673
 
12.5%
,663
 
12.3%
5378
 
7.0%
t336
 
6.2%
o335
 
6.2%
2328
 
6.1%
1295
 
5.5%
e3
 
0.1%
Other values (8)12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2684
49.9%
Lowercase Letter683
 
12.7%
Space Separator674
 
12.5%
Currency Symbol673
 
12.5%
Other Punctuation663
 
12.3%
Uppercase Letter3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.2%
o335
49.0%
e3
 
0.4%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
01683
62.7%
5378
 
14.1%
2328
 
12.2%
1295
 
11.0%
ValueCountFrequency (%)
O2
66.7%
L1
33.3%
ValueCountFrequency (%)
$673
100.0%
ValueCountFrequency (%)
,663
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4694
87.2%
Latin686
 
12.8%

Most frequent character per script

ValueCountFrequency (%)
t336
49.0%
o335
48.8%
e3
 
0.4%
O2
 
0.3%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
L1
 
0.1%
h1
 
0.1%
a1
 
0.1%
ValueCountFrequency (%)
01683
35.9%
674
14.4%
$673
 
14.3%
,663
 
14.1%
5378
 
8.1%
2328
 
7.0%
1295
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5380
100.0%

Most frequent character per block

ValueCountFrequency (%)
01683
31.3%
674
12.5%
$673
 
12.5%
,663
 
12.3%
5378
 
7.0%
t336
 
6.2%
o335
 
6.2%
2328
 
6.1%
1295
 
5.5%
e3
 
0.1%
Other values (8)12
 
0.2%

Location Employee Size Actual
Real number (ℝ≥0)

Distinct36
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.65889213
Minimum6
Maximum1000
Zeros0
Zeros (%)0.0%
Memory size2.8 KiB
2021-01-21T00:52:28.296154image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q120
median25
Q325
95-th percentile40
Maximum1000
Range994
Interquartile range (IQR)5

Descriptive statistics

Standard deviation89.78354559
Coefficient of variation (CV)2.749130168
Kurtosis109.5991316
Mean32.65889213
Median Absolute Deviation (MAD)3
Skewness10.49190821
Sum11202
Variance8061.08506
MonotocityNot monotonic
2021-01-21T00:52:28.524101image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
25115
33.5%
2031
 
9.0%
3023
 
6.7%
2219
 
5.5%
1516
 
4.7%
3513
 
3.8%
2311
 
3.2%
2411
 
3.2%
1810
 
2.9%
1410
 
2.9%
Other values (26)84
24.5%
ValueCountFrequency (%)
62
0.6%
72
0.6%
103
0.9%
123
0.9%
133
0.9%
ValueCountFrequency (%)
10002
0.6%
9501
 
0.3%
603
0.9%
551
 
0.3%
503
0.9%

Location Employee Size Range
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
20 to 49
264 
10 to 19
65 
50 to 99
 
7
5 to 9
 
4
1000 to 4999
 
2

Length

Max length12
Median length8
Mean length8.005830904
Min length6

Characters and Unicode

Total characters2746
Distinct characters9
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row20 to 49
2nd row20 to 49
3rd row20 to 49
4th row20 to 49
5th row20 to 49
ValueCountFrequency (%)
20 to 49264
77.0%
10 to 1965
 
19.0%
50 to 997
 
2.0%
5 to 94
 
1.2%
1000 to 49992
 
0.6%
500 to 9991
 
0.3%
2021-01-21T00:52:28.955606image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:29.102723image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to343
33.3%
20264
25.7%
49264
25.7%
1965
 
6.3%
1065
 
6.3%
507
 
0.7%
997
 
0.7%
94
 
0.4%
54
 
0.4%
49992
 
0.2%
Other values (3)4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
686
25.0%
9356
13.0%
0344
12.5%
t343
12.5%
o343
12.5%
4266
 
9.7%
2264
 
9.6%
1132
 
4.8%
512
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1374
50.0%
Space Separator686
25.0%
Lowercase Letter686
25.0%

Most frequent character per category

ValueCountFrequency (%)
9356
25.9%
0344
25.0%
4266
19.4%
2264
19.2%
1132
 
9.6%
512
 
0.9%
ValueCountFrequency (%)
t343
50.0%
o343
50.0%
ValueCountFrequency (%)
686
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2060
75.0%
Latin686
 
25.0%

Most frequent character per script

ValueCountFrequency (%)
686
33.3%
9356
17.3%
0344
16.7%
4266
 
12.9%
2264
 
12.8%
1132
 
6.4%
512
 
0.6%
ValueCountFrequency (%)
t343
50.0%
o343
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2746
100.0%

Most frequent character per block

ValueCountFrequency (%)
686
25.0%
9356
13.0%
0344
12.5%
t343
12.5%
o343
12.5%
4266
 
9.7%
2264
 
9.6%
1132
 
4.8%
512
 
0.4%

Location Sales Volume Actual
Categorical

HIGH CARDINALITY

Distinct125
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
$1,128,000
 
20
$1,299,000
 
17
$1,186,000
 
15
$1,303,000
 
13
$1,318,000
 
9
Other values (120)
269 

Length

Max length10
Median length10
Mean length9.469387755
Min length2

Characters and Unicode

Total characters3248
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)19.0%

Sample

1st row$1,180,000
2nd row$1,186,000
3rd row$1,186,000
4th row$1,405,000
5th row$1,660,000
ValueCountFrequency (%)
$1,128,00020
 
5.8%
$1,299,00017
 
5.0%
$1,186,00015
 
4.4%
$1,303,00013
 
3.8%
$1,318,0009
 
2.6%
$2,447,0009
 
2.6%
$1,039,0008
 
2.3%
$1,162,0008
 
2.3%
$1,818,0007
 
2.0%
$1,481,0007
 
2.0%
Other values (115)230
67.1%
2021-01-21T00:52:29.612261image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1,128,00020
 
5.8%
1,299,00017
 
5.0%
1,186,00015
 
4.4%
1,303,00013
 
3.8%
1,318,0009
 
2.6%
2,447,0009
 
2.6%
1,162,0008
 
2.3%
1,039,0008
 
2.3%
1,818,0007
 
2.0%
1,481,0007
 
2.0%
Other values (115)230
67.1%

Most occurring characters

ValueCountFrequency (%)
01098
33.8%
,607
18.7%
1388
 
11.9%
$343
 
10.6%
8127
 
3.9%
3119
 
3.7%
2110
 
3.4%
4109
 
3.4%
999
 
3.0%
585
 
2.6%
Other values (2)163
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2298
70.8%
Other Punctuation607
 
18.7%
Currency Symbol343
 
10.6%

Most frequent character per category

ValueCountFrequency (%)
01098
47.8%
1388
 
16.9%
8127
 
5.5%
3119
 
5.2%
2110
 
4.8%
4109
 
4.7%
999
 
4.3%
585
 
3.7%
684
 
3.7%
779
 
3.4%
ValueCountFrequency (%)
$343
100.0%
ValueCountFrequency (%)
,607
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common3248
100.0%

Most frequent character per script

ValueCountFrequency (%)
01098
33.8%
,607
18.7%
1388
 
11.9%
$343
 
10.6%
8127
 
3.9%
3119
 
3.7%
2110
 
3.4%
4109
 
3.4%
999
 
3.0%
585
 
2.6%
Other values (2)163
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3248
100.0%

Most frequent character per block

ValueCountFrequency (%)
01098
33.8%
,607
18.7%
1388
 
11.9%
$343
 
10.6%
8127
 
3.9%
3119
 
3.7%
2110
 
3.4%
4109
 
3.4%
999
 
3.0%
585
 
2.6%
Other values (2)163
 
5.0%

Location Sales Volume Range
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)0.9%
Missing6
Missing (%)1.7%
Memory size2.8 KiB
$1-2.5 Million
270 
$500,000-1 Million
66 
Less Than $500,000
 
1

Length

Max length18
Median length14
Mean length14.79525223
Min length14

Characters and Unicode

Total characters4986
Distinct characters20
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$1-2.5 Million
2nd row$1-2.5 Million
3rd row$1-2.5 Million
4th row$1-2.5 Million
5th row$1-2.5 Million
ValueCountFrequency (%)
$1-2.5 Million270
78.7%
$500,000-1 Million66
 
19.2%
Less Than $500,0001
 
0.3%
(Missing)6
 
1.7%
2021-01-21T00:52:30.029731image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:30.171208image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
million336
49.8%
1-2.5270
40.0%
500,000-166
 
9.8%
less1
 
0.1%
500,0001
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i672
13.5%
l672
13.5%
338
 
6.8%
$337
 
6.8%
5337
 
6.8%
n337
 
6.8%
1336
 
6.7%
-336
 
6.7%
M336
 
6.7%
o336
 
6.7%
Other values (10)949
19.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter2022
40.6%
Decimal Number1278
25.6%
Space Separator338
 
6.8%
Uppercase Letter338
 
6.8%
Currency Symbol337
 
6.8%
Other Punctuation337
 
6.8%
Dash Punctuation336
 
6.7%

Most frequent character per category

ValueCountFrequency (%)
i672
33.2%
l672
33.2%
n337
16.7%
o336
16.6%
s2
 
0.1%
e1
 
< 0.1%
h1
 
< 0.1%
a1
 
< 0.1%
ValueCountFrequency (%)
5337
26.4%
1336
26.3%
0335
26.2%
2270
21.1%
ValueCountFrequency (%)
M336
99.4%
L1
 
0.3%
T1
 
0.3%
ValueCountFrequency (%)
.270
80.1%
,67
 
19.9%
ValueCountFrequency (%)
$337
100.0%
ValueCountFrequency (%)
-336
100.0%
ValueCountFrequency (%)
338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2626
52.7%
Latin2360
47.3%

Most frequent character per script

ValueCountFrequency (%)
i672
28.5%
l672
28.5%
n337
14.3%
M336
14.2%
o336
14.2%
s2
 
0.1%
L1
 
< 0.1%
e1
 
< 0.1%
T1
 
< 0.1%
h1
 
< 0.1%
ValueCountFrequency (%)
338
12.9%
$337
12.8%
5337
12.8%
1336
12.8%
-336
12.8%
0335
12.8%
2270
10.3%
.270
10.3%
,67
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII4986
100.0%

Most frequent character per block

ValueCountFrequency (%)
i672
13.5%
l672
13.5%
338
 
6.8%
$337
 
6.8%
5337
 
6.8%
n337
 
6.8%
1336
 
6.7%
-336
 
6.7%
M336
 
6.7%
o336
 
6.7%
Other values (10)949
19.0%
Distinct5
Distinct (%)1.5%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$5,000 to $10,000
243 
$10,000 to $25,000
71 
$2,500 to $5,000
 
21
Over $100,000
 
2
Less than $2,500
 
1

Length

Max length18
Median length17
Mean length17.12130178
Min length13

Characters and Unicode

Total characters5787
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$5,000 to $10,000
2nd row$5,000 to $10,000
3rd row$5,000 to $10,000
4th row$5,000 to $10,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$5,000 to $10,000243
70.8%
$10,000 to $25,00071
 
20.7%
$2,500 to $5,00021
 
6.1%
Over $100,0002
 
0.6%
Less than $2,5001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:30.522295image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:30.658051image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to335
33.1%
10,000314
31.0%
5,000264
26.1%
25,00071
 
7.0%
2,50022
 
2.2%
over2
 
0.2%
100,0002
 
0.2%
less1
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02315
40.0%
674
 
11.6%
$673
 
11.6%
,673
 
11.6%
5357
 
6.2%
t336
 
5.8%
o335
 
5.8%
1316
 
5.5%
293
 
1.6%
e3
 
0.1%
Other values (8)12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3081
53.2%
Lowercase Letter683
 
11.8%
Space Separator674
 
11.6%
Currency Symbol673
 
11.6%
Other Punctuation673
 
11.6%
Uppercase Letter3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.2%
o335
49.0%
e3
 
0.4%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
02315
75.1%
5357
 
11.6%
1316
 
10.3%
293
 
3.0%
ValueCountFrequency (%)
O2
66.7%
L1
33.3%
ValueCountFrequency (%)
$673
100.0%
ValueCountFrequency (%)
,673
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5101
88.1%
Latin686
 
11.9%

Most frequent character per script

ValueCountFrequency (%)
t336
49.0%
o335
48.8%
e3
 
0.4%
O2
 
0.3%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
L1
 
0.1%
h1
 
0.1%
a1
 
0.1%
ValueCountFrequency (%)
02315
45.4%
674
 
13.2%
$673
 
13.2%
,673
 
13.2%
5357
 
7.0%
1316
 
6.2%
293
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII5787
100.0%

Most frequent character per block

ValueCountFrequency (%)
02315
40.0%
674
 
11.6%
$673
 
11.6%
,673
 
11.6%
5357
 
6.2%
t336
 
5.8%
o335
 
5.8%
1316
 
5.5%
293
 
1.6%
e3
 
0.1%
Other values (8)12
 
0.2%

Office Supplies Expense
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.2%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$20,000 to $50,000
265 
$10,000 to $20,000
70 
Over $250,000
 
2
$5,000 to $10,000
 
1

Length

Max length18
Median length18
Mean length17.96745562
Min length13

Characters and Unicode

Total characters6073
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$20,000 to $50,000
2nd row$20,000 to $50,000
3rd row$20,000 to $50,000
4th row$20,000 to $50,000
5th row$20,000 to $50,000
ValueCountFrequency (%)
$20,000 to $50,000265
77.3%
$10,000 to $20,00070
 
20.4%
Over $250,0002
 
0.6%
$5,000 to $10,0001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:31.161916image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:31.316137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to336
33.2%
20,000335
33.1%
50,000265
26.2%
10,00071
 
7.0%
250,0002
 
0.2%
over2
 
0.2%
5,0001
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02695
44.4%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
2337
 
5.5%
t336
 
5.5%
o336
 
5.5%
5268
 
4.4%
171
 
1.2%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3371
55.5%
Lowercase Letter678
 
11.2%
Currency Symbol674
 
11.1%
Other Punctuation674
 
11.1%
Space Separator674
 
11.1%
Uppercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.6%
o336
49.6%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02695
79.9%
2337
 
10.0%
5268
 
8.0%
171
 
2.1%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,674
100.0%
ValueCountFrequency (%)
674
100.0%
ValueCountFrequency (%)
O2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5393
88.8%
Latin680
 
11.2%

Most frequent character per script

ValueCountFrequency (%)
02695
50.0%
$674
 
12.5%
,674
 
12.5%
674
 
12.5%
2337
 
6.2%
5268
 
5.0%
171
 
1.3%
ValueCountFrequency (%)
t336
49.4%
o336
49.4%
O2
 
0.3%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII6073
100.0%

Most frequent character per block

ValueCountFrequency (%)
02695
44.4%
$674
 
11.1%
,674
 
11.1%
674
 
11.1%
2337
 
5.5%
t336
 
5.5%
o336
 
5.5%
5268
 
4.4%
171
 
1.2%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Own or Lease
Categorical

HIGH CORRELATION
MISSING

Distinct3
Distinct (%)2.3%
Missing215
Missing (%)62.7%
Memory size2.8 KiB
Lease
48 
Own
43 
Unknown
37 

Length

Max length7
Median length5
Mean length4.90625
Min length3

Characters and Unicode

Total characters628
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOwn
2nd rowUnknown
3rd rowOwn
4th rowLease
5th rowOwn
ValueCountFrequency (%)
Lease48
 
14.0%
Own43
 
12.5%
Unknown37
 
10.8%
(Missing)215
62.7%
2021-01-21T00:52:31.799607image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:32.345684image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
lease48
37.5%
own43
33.6%
unknown37
28.9%

Most occurring characters

ValueCountFrequency (%)
n154
24.5%
e96
15.3%
w80
12.7%
L48
 
7.6%
a48
 
7.6%
s48
 
7.6%
O43
 
6.8%
U37
 
5.9%
k37
 
5.9%
o37
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter500
79.6%
Uppercase Letter128
 
20.4%

Most frequent character per category

ValueCountFrequency (%)
n154
30.8%
e96
19.2%
w80
16.0%
a48
 
9.6%
s48
 
9.6%
k37
 
7.4%
o37
 
7.4%
ValueCountFrequency (%)
L48
37.5%
O43
33.6%
U37
28.9%

Most occurring scripts

ValueCountFrequency (%)
Latin628
100.0%

Most frequent character per script

ValueCountFrequency (%)
n154
24.5%
e96
15.3%
w80
12.7%
L48
 
7.6%
a48
 
7.6%
s48
 
7.6%
O43
 
6.8%
U37
 
5.9%
k37
 
5.9%
o37
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII628
100.0%

Most frequent character per block

ValueCountFrequency (%)
n154
24.5%
e96
15.3%
w80
12.7%
L48
 
7.6%
a48
 
7.6%
s48
 
7.6%
O43
 
6.8%
U37
 
5.9%
k37
 
5.9%
o37
 
5.9%

Package Container Expense
Categorical

MISSING

Distinct4
Distinct (%)1.2%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$10,000 to $25,000
242 
$5,000 to $10,000
88 
$1,000 to $5,000
 
6
Over $50,000
 
2

Length

Max length18
Median length18
Mean length17.66863905
Min length12

Characters and Unicode

Total characters5972
Distinct characters13
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$10,000 to $25,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$10,000 to $25,000
ValueCountFrequency (%)
$10,000 to $25,000242
70.6%
$5,000 to $10,00088
 
25.7%
$1,000 to $5,0006
 
1.7%
Over $50,0002
 
0.6%
(Missing)5
 
1.5%
2021-01-21T00:52:32.752359image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:32.903137image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to336
33.2%
10,000330
32.6%
25,000242
23.9%
5,00094
 
9.3%
1,0006
 
0.6%
50,0002
 
0.2%
over2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
02354
39.4%
$674
 
11.3%
,674
 
11.3%
674
 
11.3%
5338
 
5.7%
1336
 
5.6%
t336
 
5.6%
o336
 
5.6%
2242
 
4.1%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3270
54.8%
Lowercase Letter678
 
11.4%
Currency Symbol674
 
11.3%
Other Punctuation674
 
11.3%
Space Separator674
 
11.3%
Uppercase Letter2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.6%
o336
49.6%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02354
72.0%
5338
 
10.3%
1336
 
10.3%
2242
 
7.4%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,674
100.0%
ValueCountFrequency (%)
674
100.0%
ValueCountFrequency (%)
O2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5292
88.6%
Latin680
 
11.4%

Most frequent character per script

ValueCountFrequency (%)
02354
44.5%
$674
 
12.7%
,674
 
12.7%
674
 
12.7%
5338
 
6.4%
1336
 
6.3%
2242
 
4.6%
ValueCountFrequency (%)
t336
49.4%
o336
49.4%
O2
 
0.3%
v2
 
0.3%
e2
 
0.3%
r2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII5972
100.0%

Most frequent character per block

ValueCountFrequency (%)
02354
39.4%
$674
 
11.3%
,674
 
11.3%
674
 
11.3%
5338
 
5.7%
1336
 
5.6%
t336
 
5.6%
o336
 
5.6%
2242
 
4.1%
O2
 
< 0.1%
Other values (3)6
 
0.1%

Payroll and Benefits Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct5
Distinct (%)1.5%
Missing4
Missing (%)1.2%
Memory size2.8 KiB
$250,000 to $500,000
253 
$100,000 to $250,000
43 
$500,000 to $1 Million
39 
Over $10 Million
 
3
Less than $100,000
 
1

Length

Max length22
Median length20
Mean length20.18879056
Min length16

Characters and Unicode

Total characters6844
Distinct characters21
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$250,000 to $500,000
2nd row$250,000 to $500,000
3rd row$250,000 to $500,000
4th row$250,000 to $500,000
5th row$250,000 to $500,000
ValueCountFrequency (%)
$250,000 to $500,000253
73.8%
$100,000 to $250,00043
 
12.5%
$500,000 to $1 Million39
 
11.4%
Over $10 Million3
 
0.9%
Less than $100,0001
 
0.3%
(Missing)4
 
1.2%
2021-01-21T00:52:33.390959image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:33.555073image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to335
31.7%
250,000296
28.0%
500,000292
27.7%
100,00044
 
4.2%
million42
 
4.0%
139
 
3.7%
103
 
0.3%
over3
 
0.3%
less1
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
02867
41.9%
717
 
10.5%
$674
 
9.8%
,632
 
9.2%
5588
 
8.6%
o377
 
5.5%
t336
 
4.9%
2296
 
4.3%
186
 
1.3%
i84
 
1.2%
Other values (11)187
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3837
56.1%
Lowercase Letter938
 
13.7%
Space Separator717
 
10.5%
Currency Symbol674
 
9.8%
Other Punctuation632
 
9.2%
Uppercase Letter46
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
o377
40.2%
t336
35.8%
i84
 
9.0%
l84
 
9.0%
n43
 
4.6%
e4
 
0.4%
v3
 
0.3%
r3
 
0.3%
s2
 
0.2%
h1
 
0.1%
ValueCountFrequency (%)
02867
74.7%
5588
 
15.3%
2296
 
7.7%
186
 
2.2%
ValueCountFrequency (%)
M42
91.3%
O3
 
6.5%
L1
 
2.2%
ValueCountFrequency (%)
$674
100.0%
ValueCountFrequency (%)
,632
100.0%
ValueCountFrequency (%)
717
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5860
85.6%
Latin984
 
14.4%

Most frequent character per script

ValueCountFrequency (%)
o377
38.3%
t336
34.1%
i84
 
8.5%
l84
 
8.5%
n43
 
4.4%
M42
 
4.3%
e4
 
0.4%
O3
 
0.3%
v3
 
0.3%
r3
 
0.3%
Other values (4)5
 
0.5%
ValueCountFrequency (%)
02867
48.9%
717
 
12.2%
$674
 
11.5%
,632
 
10.8%
5588
 
10.0%
2296
 
5.1%
186
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6844
100.0%

Most frequent character per block

ValueCountFrequency (%)
02867
41.9%
717
 
10.5%
$674
 
9.8%
,632
 
9.2%
5588
 
8.6%
o377
 
5.5%
t336
 
4.9%
2296
 
4.3%
186
 
1.3%
i84
 
1.2%
Other values (11)187
 
2.7%

Purchase Print Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.2%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$1,000 to $2,500
273 
$500 to $1,000
62 
Over $25,000
 
2
Less than $500
 
1

Length

Max length16
Median length16
Mean length15.6035503
Min length12

Characters and Unicode

Total characters5274
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row$1,000 to $2,500
2nd row$1,000 to $2,500
3rd row$1,000 to $2,500
4th row$1,000 to $2,500
5th row$1,000 to $2,500
ValueCountFrequency (%)
$1,000 to $2,500273
79.6%
$500 to $1,00062
 
18.1%
Over $25,0002
 
0.6%
Less than $5001
 
0.3%
(Missing)5
 
1.5%
2021-01-21T00:52:34.050586image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:34.199202image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
1,000335
33.1%
to335
33.1%
2,500273
27.0%
50063
 
6.2%
25,0002
 
0.2%
over2
 
0.2%
less1
 
0.1%
than1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
01683
31.9%
674
12.8%
$673
 
12.8%
,610
 
11.6%
5338
 
6.4%
t336
 
6.4%
1335
 
6.4%
o335
 
6.4%
2275
 
5.2%
e3
 
0.1%
Other values (8)12
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2631
49.9%
Lowercase Letter683
 
13.0%
Space Separator674
 
12.8%
Currency Symbol673
 
12.8%
Other Punctuation610
 
11.6%
Uppercase Letter3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
t336
49.2%
o335
49.0%
e3
 
0.4%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
h1
 
0.1%
a1
 
0.1%
n1
 
0.1%
ValueCountFrequency (%)
01683
64.0%
5338
 
12.8%
1335
 
12.7%
2275
 
10.5%
ValueCountFrequency (%)
O2
66.7%
L1
33.3%
ValueCountFrequency (%)
$673
100.0%
ValueCountFrequency (%)
,610
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4588
87.0%
Latin686
 
13.0%

Most frequent character per script

ValueCountFrequency (%)
t336
49.0%
o335
48.8%
e3
 
0.4%
O2
 
0.3%
v2
 
0.3%
r2
 
0.3%
s2
 
0.3%
L1
 
0.1%
h1
 
0.1%
a1
 
0.1%
ValueCountFrequency (%)
01683
36.7%
674
14.7%
$673
 
14.7%
,610
 
13.3%
5338
 
7.4%
1335
 
7.3%
2275
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII5274
100.0%

Most frequent character per block

ValueCountFrequency (%)
01683
31.9%
674
12.8%
$673
 
12.8%
,610
 
11.6%
5338
 
6.4%
t336
 
6.4%
1335
 
6.4%
o335
 
6.4%
2275
 
5.2%
e3
 
0.1%
Other values (8)12
 
0.2%

Rent Expenses
Categorical

MISSING

Distinct6
Distinct (%)1.8%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$10,000 to $25,000
202 
$50,000 to $100,000
72 
$25,000 to $50,000
27 
Less than $10,000
24 
$100,000 to $250,000
 
11

Length

Max length20
Median length18
Mean length18.17751479
Min length13

Characters and Unicode

Total characters6144
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$10,000 to $25,000
2nd row$50,000 to $100,000
3rd row$10,000 to $25,000
4th row$10,000 to $25,000
5th row$50,000 to $100,000
ValueCountFrequency (%)
$10,000 to $25,000202
58.9%
$50,000 to $100,00072
 
21.0%
$25,000 to $50,00027
 
7.9%
Less than $10,00024
 
7.0%
$100,000 to $250,00011
 
3.2%
Over $500,0002
 
0.6%
(Missing)5
 
1.5%
2021-01-21T00:52:34.668108image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:34.816929image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to312
30.8%
25,000229
22.6%
10,000226
22.3%
50,00099
 
9.8%
100,00083
 
8.2%
less24
 
2.4%
than24
 
2.4%
250,00011
 
1.1%
500,0002
 
0.2%
over2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
02456
40.0%
674
 
11.0%
$650
 
10.6%
,650
 
10.6%
5341
 
5.6%
t336
 
5.5%
o312
 
5.1%
1309
 
5.0%
2240
 
3.9%
s48
 
0.8%
Other values (8)128
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number3346
54.5%
Lowercase Letter798
 
13.0%
Space Separator674
 
11.0%
Currency Symbol650
 
10.6%
Other Punctuation650
 
10.6%
Uppercase Letter26
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
t336
42.1%
o312
39.1%
s48
 
6.0%
e26
 
3.3%
h24
 
3.0%
a24
 
3.0%
n24
 
3.0%
v2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02456
73.4%
5341
 
10.2%
1309
 
9.2%
2240
 
7.2%
ValueCountFrequency (%)
L24
92.3%
O2
 
7.7%
ValueCountFrequency (%)
$650
100.0%
ValueCountFrequency (%)
,650
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common5320
86.6%
Latin824
 
13.4%

Most frequent character per script

ValueCountFrequency (%)
t336
40.8%
o312
37.9%
s48
 
5.8%
e26
 
3.2%
L24
 
2.9%
h24
 
2.9%
a24
 
2.9%
n24
 
2.9%
O2
 
0.2%
v2
 
0.2%
ValueCountFrequency (%)
02456
46.2%
674
 
12.7%
$650
 
12.2%
,650
 
12.2%
5341
 
6.4%
1309
 
5.8%
2240
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII6144
100.0%

Most frequent character per block

ValueCountFrequency (%)
02456
40.0%
674
 
11.0%
$650
 
10.6%
,650
 
10.6%
5341
 
5.6%
t336
 
5.5%
o312
 
5.1%
1309
 
5.0%
2240
 
3.9%
s48
 
0.8%
Other values (8)128
 
2.1%

Square Footage
Categorical

Distinct5
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size2.8 KiB
2,500 - 4,999
320 
1 - 1,499
 
18
100,000+
 
3
10,000 - 19,999
 
1
40,000 - 99,999
 
1

Length

Max length15
Median length13
Mean length12.75801749
Min length8

Characters and Unicode

Total characters4376
Distinct characters10
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.6%

Sample

1st row2,500 - 4,999
2nd row2,500 - 4,999
3rd row2,500 - 4,999
4th row2,500 - 4,999
5th row2,500 - 4,999
ValueCountFrequency (%)
2,500 - 4,999320
93.3%
1 - 1,49918
 
5.2%
100,000+3
 
0.9%
10,000 - 19,9991
 
0.3%
40,000 - 99,9991
 
0.3%
2021-01-21T00:52:35.363829image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:35.509876image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
340
33.2%
4,999320
31.3%
2,500320
31.3%
118
 
1.8%
1,49918
 
1.8%
100,0003
 
0.3%
10,0001
 
0.1%
99,9991
 
0.1%
19,9991
 
0.1%
40,0001
 
0.1%

Most occurring characters

ValueCountFrequency (%)
91005
23.0%
680
15.5%
,665
15.2%
0663
15.2%
-340
 
7.8%
4339
 
7.7%
2320
 
7.3%
5320
 
7.3%
141
 
0.9%
+3
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2688
61.4%
Space Separator680
 
15.5%
Other Punctuation665
 
15.2%
Dash Punctuation340
 
7.8%
Math Symbol3
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
91005
37.4%
0663
24.7%
4339
 
12.6%
2320
 
11.9%
5320
 
11.9%
141
 
1.5%
ValueCountFrequency (%)
,665
100.0%
ValueCountFrequency (%)
680
100.0%
ValueCountFrequency (%)
-340
100.0%
ValueCountFrequency (%)
+3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4376
100.0%

Most frequent character per script

ValueCountFrequency (%)
91005
23.0%
680
15.5%
,665
15.2%
0663
15.2%
-340
 
7.8%
4339
 
7.7%
2320
 
7.3%
5320
 
7.3%
141
 
0.9%
+3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII4376
100.0%

Most frequent character per block

ValueCountFrequency (%)
91005
23.0%
680
15.5%
,665
15.2%
0663
15.2%
-340
 
7.8%
4339
 
7.7%
2320
 
7.3%
5320
 
7.3%
141
 
0.9%
+3
 
0.1%

Telcom Expenses
Categorical

HIGH CORRELATION
MISSING

Distinct4
Distinct (%)1.2%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$2,000 to $5,000
283 
$5,000 to $20,000
39 
Less than $2,000
 
14
Over $250,000
 
2

Length

Max length17
Median length16
Mean length16.09763314
Min length13

Characters and Unicode

Total characters5441
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$2,000 to $5,000
3rd row$2,000 to $5,000
4th row$2,000 to $5,000
5th row$2,000 to $5,000
ValueCountFrequency (%)
$2,000 to $5,000283
82.5%
$5,000 to $20,00039
 
11.4%
Less than $2,00014
 
4.1%
Over $250,0002
 
0.6%
(Missing)5
 
1.5%
2021-01-21T00:52:35.913364image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:36.066321image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
5,000322
31.8%
to322
31.8%
2,000297
29.3%
20,00039
 
3.9%
less14
 
1.4%
than14
 
1.4%
250,0002
 
0.2%
over2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
02021
37.1%
674
 
12.4%
$660
 
12.1%
,660
 
12.1%
2338
 
6.2%
t336
 
6.2%
5324
 
6.0%
o322
 
5.9%
s28
 
0.5%
e16
 
0.3%
Other values (7)62
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2683
49.3%
Lowercase Letter748
 
13.7%
Space Separator674
 
12.4%
Currency Symbol660
 
12.1%
Other Punctuation660
 
12.1%
Uppercase Letter16
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
t336
44.9%
o322
43.0%
s28
 
3.7%
e16
 
2.1%
h14
 
1.9%
a14
 
1.9%
n14
 
1.9%
v2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02021
75.3%
2338
 
12.6%
5324
 
12.1%
ValueCountFrequency (%)
L14
87.5%
O2
 
12.5%
ValueCountFrequency (%)
$660
100.0%
ValueCountFrequency (%)
,660
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4677
86.0%
Latin764
 
14.0%

Most frequent character per script

ValueCountFrequency (%)
t336
44.0%
o322
42.1%
s28
 
3.7%
e16
 
2.1%
L14
 
1.8%
h14
 
1.8%
a14
 
1.8%
n14
 
1.8%
O2
 
0.3%
v2
 
0.3%
ValueCountFrequency (%)
02021
43.2%
674
 
14.4%
$660
 
14.1%
,660
 
14.1%
2338
 
7.2%
5324
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII5441
100.0%

Most frequent character per block

ValueCountFrequency (%)
02021
37.1%
674
 
12.4%
$660
 
12.1%
,660
 
12.1%
2338
 
6.2%
t336
 
6.2%
5324
 
6.0%
o322
 
5.9%
s28
 
0.5%
e16
 
0.3%
Other values (7)62
 
1.1%

Utilities Expenses
Categorical

MISSING

Distinct7
Distinct (%)2.1%
Missing5
Missing (%)1.5%
Memory size2.8 KiB
$2,000 to $5,000
199 
$25,000 to $50,000
68 
$5,000 to $10,000
23 
$10,000 to $25,000
22 
Less than $2,000
 
15
Other values (2)
 
11

Length

Max length19
Median length16
Mean length16.66272189
Min length13

Characters and Unicode

Total characters5632
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row$2,000 to $5,000
2nd row$25,000 to $50,000
3rd row$2,000 to $5,000
4th row$2,000 to $5,000
5th row$25,000 to $50,000
ValueCountFrequency (%)
$2,000 to $5,000199
58.0%
$25,000 to $50,00068
 
19.8%
$5,000 to $10,00023
 
6.7%
$10,000 to $25,00022
 
6.4%
Less than $2,00015
 
4.4%
$50,000 to $100,0009
 
2.6%
Over $100,0002
 
0.6%
(Missing)5
 
1.5%
2021-01-21T00:52:36.522034image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
2021-01-21T00:52:36.676866image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
ValueCountFrequency (%)
to321
31.7%
5,000222
21.9%
2,000214
21.1%
25,00090
 
8.9%
50,00077
 
7.6%
10,00045
 
4.4%
less15
 
1.5%
than15
 
1.5%
100,00011
 
1.1%
over2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
02121
37.7%
674
 
12.0%
$659
 
11.7%
,659
 
11.7%
5389
 
6.9%
t336
 
6.0%
o321
 
5.7%
2304
 
5.4%
156
 
1.0%
s30
 
0.5%
Other values (8)83
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2870
51.0%
Lowercase Letter753
 
13.4%
Space Separator674
 
12.0%
Currency Symbol659
 
11.7%
Other Punctuation659
 
11.7%
Uppercase Letter17
 
0.3%

Most frequent character per category

ValueCountFrequency (%)
t336
44.6%
o321
42.6%
s30
 
4.0%
e17
 
2.3%
h15
 
2.0%
a15
 
2.0%
n15
 
2.0%
v2
 
0.3%
r2
 
0.3%
ValueCountFrequency (%)
02121
73.9%
5389
 
13.6%
2304
 
10.6%
156
 
2.0%
ValueCountFrequency (%)
L15
88.2%
O2
 
11.8%
ValueCountFrequency (%)
$659
100.0%
ValueCountFrequency (%)
,659
100.0%
ValueCountFrequency (%)
674
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common4862
86.3%
Latin770
 
13.7%

Most frequent character per script

ValueCountFrequency (%)
t336
43.6%
o321
41.7%
s30
 
3.9%
e17
 
2.2%
L15
 
1.9%
h15
 
1.9%
a15
 
1.9%
n15
 
1.9%
O2
 
0.3%
v2
 
0.3%
ValueCountFrequency (%)
02121
43.6%
674
 
13.9%
$659
 
13.6%
,659
 
13.6%
5389
 
8.0%
2304
 
6.3%
156
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII5632
100.0%

Most frequent character per block

ValueCountFrequency (%)
02121
37.7%
674
 
12.0%
$659
 
11.7%
,659
 
11.7%
5389
 
6.9%
t336
 
6.0%
o321
 
5.7%
2304
 
5.4%
156
 
1.0%
s30
 
0.5%
Other values (8)83
 
1.5%

Interactions

2021-01-21T00:52:06.585688image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:06.766632image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:06.934552image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:07.128804image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:07.314399image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:07.506380image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:07.686705image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:07.890826image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:08.082310image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:08.264721image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:08.449613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:09.293230image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:09.490468image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:09.674779image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:09.853672image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:10.035054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:10.236588image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:10.436054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:10.620401image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:10.826730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.035307image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.228513image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.413418image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.588300image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.788594image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:11.980834image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:12.170000image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:12.359931image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:12.540730image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-21T00:52:12.747852image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-21T00:52:37.015012image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-21T00:52:37.282809image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-21T00:52:37.547639image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-21T00:52:37.903260image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-01-21T00:52:38.574741image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-01-21T00:52:13.333304image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-21T00:52:15.585536image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-21T00:52:16.371243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-21T00:52:17.429837image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Advertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
0$20,000 to $50,000$2,500 to $5,0001104 Sunrise HwyAmityvilleNYSuffolkNw Yrk, NY-NJ-PANorth Amityville117012513.0NaN30Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002120 to 49$1,180,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
1$20,000 to $50,000$2,500 to $5,0003310 21st StAstoriaNYQueensNw Yrk, NY-NJ-PAAstoria111064239.0NaN7Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002520 to 49$1,186,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
2$20,000 to $50,000$2,500 to $5,0003310 Astoria BlvdAstoriaNYQueensNw Yrk, NY-NJ-PAAstoria111034412.0NaN24Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002520 to 49$1,186,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
3$20,000 to $50,000$2,500 to $5,000335 E Main StBay ShoreNYSuffolkNw Yrk, NY-NJ-PABay Shore117068410.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002520 to 49$1,405,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
4$20,000 to $50,000$2,500 to $5,00022210 Northern BlvdBaysideNYQueensNw Yrk, NY-NJ-PABayside113613640.0NaN37Burger King$2,500 to $5,000$1,000 to $10,000$0$25,000 to $50,000202011$1,000 to $2,5003520 to 49$1,660,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
5$20,000 to $50,000$2,500 to $5,0004201 Hempstead TpkeBethpageNYNassauNw Yrk, NY-NJ-PAPlainedge117145701.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002420 to 49$1,357,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000Unknown$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
6$20,000 to $50,000$1,000 to $2,50032 Montauk HwyBlue PointNYSuffolkNw Yrk, NY-NJ-PABlue Point117151139.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5001610 to 19$899,000$500,000-1 Million$5,000 to $10,000$10,000 to $20,000NaN$5,000 to $10,000$250,000 to $500,000$500 to $1,000$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
7$20,000 to $50,000$2,500 to $5,0005141 Sunrise HwyBohemiaNYSuffolkNw Yrk, NY-NJ-PABohemia117164615.0NaN20Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002720 to 49$1,517,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$5,000 to $10,000
8$20,000 to $50,000$2,500 to $5,0001519 Route 22BrewsterNYPutnamNw Yrk, NY-NJ-PANaN105094009.0NaN33Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5001810 to 19$958,000$500,000-1 Million$5,000 to $10,000$10,000 to $20,000Own$5,000 to $10,000$250,000 to $500,000$500 to $1,000Less than $10,0002,500 - 4,999$2,000 to $5,000Less than $2,000
9$20,000 to $50,000$2,500 to $5,000Route 22BrewsterNYPutnamNw Yrk, NY-NJ-PANaN10509NaNNaN10Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202002$1,000 to $2,5002520 to 49$1,331,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000

Last rows

Advertising ExpensesAccounting ExpensesAddressCityStateCountyMetro AreaNeighborhoodZIP CodeZIP FourYear EstablishedYears In DatabaseCompany NameComputer ExpensesContract Labor ExpensesCorporate Sales Volume ActualInsurance ExpensesLast Updated OnLegal ExpensesLocation Employee Size ActualLocation Employee Size RangeLocation Sales Volume ActualLocation Sales Volume RangeManagement/Administration ExpensesOffice Supplies ExpenseOwn or LeasePackage Container ExpensePayroll and Benefits ExpensesPurchase Print ExpensesRent ExpensesSquare FootageTelcom ExpensesUtilities Expenses
333$20,000 to $50,000$2,500 to $5,0005501 Corporate WayWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334072023.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002020 to 49$1,055,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000Lease$5,000 to $10,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
334$10,000 to $20,000$1,000 to $2,5006903 Okeechobee BlvdWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLRiverwalk Of The Palm Beaches334112509.0NaN23Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5001510 to 19$791,000$500,000-1 Million$5,000 to $10,000$10,000 to $20,000Unknown$5,000 to $10,000$100,000 to $250,000$500 to $1,000$25,000 to $50,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
335$20,000 to $50,000$2,500 to $5,000815 S Congress AveWest Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN334064118.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002820 to 49$1,476,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Own$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
336$20,000 to $50,000$2,500 to $5,000Florida Turnpike Mile Post 94West Palm BeachFLPalm BeachMiami-Ft Ldr, FLNaN33413NaNNaN2Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002520 to 49$1,318,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$10,000 to $25,0002,500 - 4,999$2,000 to $5,000$2,000 to $5,000
337$20,000 to $50,000$2,500 to $5,0002631 S State Road 7West ParkFLBrowardMiami-Ft Ldr, FLWest Park330234101.0NaN22Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5003020 to 49$1,564,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000NaN$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
338$20,000 to $50,000$2,500 to $5,0003 Weston RdWestonFLBrowardMiami-Ft Ldr, FLNew River Estates333261110.0NaN30Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5002520 to 49$1,303,000$1-2.5 Million$5,000 to $10,000$20,000 to $50,000Lease$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
339$20,000 to $50,000$2,500 to $5,0001 W Oakland Park BlvdWilton ManorsFLBrowardMiami-Ft Ldr, FLSleepy River333112519.0NaN37Burger King$1,000 to $2,500$1,000 to $10,000$0$10,000 to $25,000202011$1,000 to $2,5003020 to 49$1,564,000$1-2.5 Million$10,000 to $25,000$20,000 to $50,000Lease$10,000 to $25,000$250,000 to $500,000$1,000 to $2,500$50,000 to $100,0002,500 - 4,999$2,000 to $5,000$25,000 to $50,000
340NaNNaN5505 Blue Lagoon DrMiamiFLMiami DadeMiami-Ft Ldr, FLBlue Lagoon East331262029.02002.015Burger King Holdings IncNaNNaN$803,666,000NaN202008NaN10001000 to 4999$0NaNNaNNaNNaNNaNOver $10 MillionNaNNaN100,000+NaNNaN
341Over $250,000Over $25,0005707 Blue Lagoon DrMiamiFLMiami DadeMiami-Ft Ldr, FLBlue Lagoon West331262015.01954.09Burger King Worldwide IncOver $50,000Over $500,000$1,146,300,000Over $100,000202008Over $25,00010001000 to 4999$0NaNOver $100,000Over $250,000NaNOver $50,000Over $10 MillionOver $25,000Over $500,000100,000+Over $250,000Over $100,000
342$5,000 to $10,000$500 to $1,0003300 E 4th Ave # 6HialeahFLMiami DadeMiami-Ft Ldr, FLBougainvillea Place330133099.02019.02Veggie Burger King CorpLess than $500$1,000 to $10,000$0$2,500 to $5,000201907Less than $50065 to 9$312,000Less Than $500,000Less than $2,500$5,000 to $10,000NaN$1,000 to $5,000Less than $100,000Less than $500$10,000 to $25,0002,500 - 4,999Less than $2,000$2,000 to $5,000